This paper presents a method for unsupervised segmentation of images consisting of multiple textures. The images under study are modeled by a proposed hierarchical random field model, which has two layers. The first l...This paper presents a method for unsupervised segmentation of images consisting of multiple textures. The images under study are modeled by a proposed hierarchical random field model, which has two layers. The first layer is modeled as a Markov Random Field (MRF) representing an unobservable region image and the second layer uses 'Filters, Random and Maximum Entropy (Abb. FRAME)' model to represent multiple textures which cover each region. Compared with the traditional Hierarchical Markov Random Field (HMRF), the FRAME can use a bigger neighborhood system and model more complex patterns. The segmentation problem is formulated as Maximum a Posteriori (MAP) estimation according to the Bayesian rule. The iterated conditional modes (ICM) algorithm is carried out to find the solution of the MAP estimation. An algorithm based on the local entropy rate is proposed to simplify the estimation of the parameters of MRF. The parameters of FRAME are estimated by the ExpectationMaximum (EM) algorithm. Finally, an experiment with synthesized and real images is given, which shows that the method can segment images with complex textures efficiently and is robust to noise.展开更多
In order to reduce the encoding complexity of macroblock coding mode decision in H.264/AVC, a selective smaller block-size searching algorithm and a selective intra coding mode searching algorithm are proposed by usin...In order to reduce the encoding complexity of macroblock coding mode decision in H.264/AVC, a selective smaller block-size searching algorithm and a selective intra coding mode searching algorithm are proposed by using the high correlation among coding modes and in spatial and temporal domains of video sequence. Simulation results demonstrate that the proposed algorithm can provide significant improvement in computational requirement, with negligible small picture quality degradation and slight bit rate increase.展开更多
文摘This paper presents a method for unsupervised segmentation of images consisting of multiple textures. The images under study are modeled by a proposed hierarchical random field model, which has two layers. The first layer is modeled as a Markov Random Field (MRF) representing an unobservable region image and the second layer uses 'Filters, Random and Maximum Entropy (Abb. FRAME)' model to represent multiple textures which cover each region. Compared with the traditional Hierarchical Markov Random Field (HMRF), the FRAME can use a bigger neighborhood system and model more complex patterns. The segmentation problem is formulated as Maximum a Posteriori (MAP) estimation according to the Bayesian rule. The iterated conditional modes (ICM) algorithm is carried out to find the solution of the MAP estimation. An algorithm based on the local entropy rate is proposed to simplify the estimation of the parameters of MRF. The parameters of FRAME are estimated by the ExpectationMaximum (EM) algorithm. Finally, an experiment with synthesized and real images is given, which shows that the method can segment images with complex textures efficiently and is robust to noise.
基金National Natural Science Foundation of China (60372018)
文摘In order to reduce the encoding complexity of macroblock coding mode decision in H.264/AVC, a selective smaller block-size searching algorithm and a selective intra coding mode searching algorithm are proposed by using the high correlation among coding modes and in spatial and temporal domains of video sequence. Simulation results demonstrate that the proposed algorithm can provide significant improvement in computational requirement, with negligible small picture quality degradation and slight bit rate increase.
